In a study published in the American Journal of Health Behavior, researchers found a nuanced link between obesity and voting behavior during the 2016 presidential election. Counties with increasing obesity rates initially showed increasing support for Donald Trump up to a certain threshold, after which this support began to wane. This suggests a complex and non-linear relationship between local obesity prevalence and political voting patterns.
Obesity has become a major public health problem in the United States. The prevalence of obesity has increased significantly and is associated with various chronic diseases and significant healthcare costs.
The condition has become a political issue with two predominant viewpoints: the “personal responsibility” perspective and the “environmental” perspective. These perspectives have different political implications and are associated with different political parties.
Although health disparities have been studied as a policy outcome, the inverse relationship, that is, how health behaviors or conditions influence policy behavior, has not been explored extensively. in-depth manner. Researchers Ruopeng An and Mengmeng Ji sought to fill this gap in the literature.
To study the link between voting behavior in the 2016 presidential election and obesity rates, researchers retrieved county-level 2016 presidential election data from the New York Times database. This data included the percentage of votes for the Republican Party presidential candidate (Donald Trump), the percentage of votes for the Democratic Party presidential candidate (Hillary Clinton), and the total number of votes in each county.
To ensure the accuracy of their election data, the researchers compared it to data from two other major election datasets maintained by The Guardian and Ballotpedia.org. They found that the state-specific percentage of votes for Republican and Democratic candidates agreed across all three databases.
County-level adult obesity prevalence data in 2013 were obtained from the U.S. Centers for Disease Control and Prevention (CDC) County Data Indicators (CDI). These data were based on the Behavioral Risk Factor Surveillance System (BRFSS), an annual survey that collects information on health behaviors, chronic diseases, and preventive services.
To account for potential influences, they controlled for a wide range of county-level sociodemographic factors, such as age groups, racial and ethnic composition, education, income, poverty rates, unemployment, metropolitan status and total population.
The researchers used a spatial modeling approach called spatial autoregressive regression (SAR) to examine the relationship between obesity rates and voting habits. This approach allowed them to take into account the spatial grouping of data, often observed in political behavior. They analyzed information from 3,111 U.S. counties, excluding Alaska due to data limitations.
Contrary to previous studies which had suggested a linear relationship between obesity rates and votes for the Republican Party candidate, this research revealed a non-linear, or more complex, link.
The study found that as a county’s obesity rate increased from about 12 percent to about 34 percent, there was a steady increase in votes for the Republican Party’s presidential candidate. However, this trend stabilized when obesity rates reached approximately 36.1%.
Beyond this point, as obesity rates continued to rise, the Republican Party candidate’s vote margin began to decline. In simpler terms, there was a turning point – a threshold in obesity rates – where a further increase in obesity did not lead to increased support for the Republican Party.
Although this study provides valuable information, it is essential to recognize its limitations. The use of county-level data means the results do not offer a direct link between individual obesity and voting behavior. Additionally, the data used for obesity rates is from 2013, which might not perfectly match the 2016 election year.
The researchers cautioned against any causal interpretation of their findings, highlighting the need for future research to explore the mechanisms underlying the relationship between obesity rates and voting behaviors.
“This study assessed the impact of county-level obesity prevalence on the 2016 presidential election,” the researchers concluded. “A quadratic association between the county’s obesity rate and the Republican presidential candidate’s vote margin was identified. The vote margin initially increased with the county’s obesity rate, but after reaching its peak, it began to decline as the obesity rate increased further. The results of this study indicate that obesity disparities can not only serve as a policy outcome but also influence political behavior. This study is observational and subject to measurement error, confounding, and ecological fallacies.
“Future research is needed to replicate the nonlinear association between county obesity rate and Republican presidential candidate vote margin identified in this study, elucidating the mechanisms by which the obesity epidemic is linked to voting behavior and track the long-term trend in obesity. the relationship between obesity rates and the presidential election.
The study, “Obesity prevalence and voting behavior in the 2016 US presidential election», was published in September 2018.
